Identifying previously-blurred areas for creating a blur effect for an image

ABSTRACT

A processing device receives input representing a selection of a first area of an image. The processing device determines whether the first selected area of the image corresponds to a blurred area previously created for a second selected area of the image. The blurred area is previously created for the second selected area of the image having a size that is less than a size of the image. The processing device replaces, responsive to determining that the first selected area of the image corresponds to the blurred area previously created for the second selected area of the image, the first selected area of the image with a corresponding portion of the blurred area previously created for the second selected area of the image.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/396,277, filed on Oct. 22, 2014, which is the U.S. National StageApplication of International Application No. PCT/CN2014/081911, filed onJul. 9, 2014. The entire contents of both above-referenced applicationsare incorporated by reference herein.

TECHNICAL FIELD

The present disclosure relates to blurring an image and, moreparticularly, to a technique of minimizing blur operations for creatinga blur effect for an image.

BACKGROUND

Blurring of an image is the appearance of the image or at least an areaof the image as out of focus. Blurring can be caused, for example, whenan object moves relative to a camera or if the object is out of focus.Blur is a commonly used effect in computer applications. There arevarious traditional methods for creating a blur effect of an image. Forexample, to apply a blur effect to an image in a computer application, aconvolution operation is typically used. Convolution is producing a newimage for the image area to be blurred by calculating weighted averagesof pixels in the image area to be blurred to create each pixel of thenew image.

Some operating systems use a framework with a blur filter to add a blureffect to an image. Examples of conventional frameworks include CoreImage, vImage, and GPUImage. These conventional frameworks create a blureffect for an image generally in the same manner. For example,conventional frameworks can allow a user to select an area of a sourceimage to be blurred, and then convolute the selected area from thesource image to create a new blurred image of the selected area, andoverlap the source image with the new blurred image to create the blureffect. Generally, for a single blur operation, the larger the size ofthe area to be blurred, the more resources (e.g., central processingunit (CPU) time and memory) it can take to produce the final result ofthe blur effect for the source image. With multiple blur operations, theresource consumption is likely to grow exponentially.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will be understood more fully from the detaileddescription given below and from the accompanying drawings of variousimplementations of the disclosure.

FIG. 1A illustrates an example blur effect on an example image.

FIG. 1B illustrates an example blur effect on an example image using adefined area, in accordance with various implementations.

FIG. 2 illustrates example system architecture in which aspects of thepresent disclosure can be implemented.

FIG. 3 illustrates an example of creating a blur effect for customshapes on an example dashboard image, in accordance with variousimplementations

FIG. 4 illustrates an example of creating a blur effect for multiplecustom shapes on an example source image by executing a single bluroperation, in accordance with various implementations.

FIG. 5 illustrates an example of minimizing the number of editingoperations for responding to multiple requests to edit the same sourceimage with the same filter, in accordance with various implementations.

FIG. 6 is a flow diagram of an implementation of a method for editing animage.

FIG. 7 is a flow diagram of an implementation of a method for replacingone or more selected areas with a corresponding portion of a resultimage.

FIG. 8 is a flow diagram of an implementation of a method for replacingone or more selected areas with a corresponding portion of a resultimage.

FIG. 9 is an example of replacing one or more selected areas with acorresponding portion of a result image, according to variousimplementations.

FIG. 10 is a block diagram of an example computer system that mayperform one or more of the operations described herein, in accordancewith various implementations.

DETAILED DESCRIPTION

The present disclosure is directed to minimizing blur operations forcreating a blur effect for an image. Implementations of the presentdisclosure perform image filtering (e.g., image blurring) moreefficiently than traditional solutions by minimizing the number ofoperations to be performed.

Referring to FIG. 1A, in a traditional solution, a user may select anarea (e.g., area 103) to blur in a source image 100. The source image100 may be, for example, a digital photo of a group of people, and theselected area 103 may include the face of a person, which the userwishes to conceal with a blur effect.

To create a blur of a selected area 103, traditional blurring solutionsconvolute 107 the selected area 103 with a blur filter (e.g., Gaussianfilter, motion filter) to produce a result image (blurred image 113) forthe selected area 103. Convolution involves calculating weightedaverages of pixels in an area (e.g., selected area 103) to create eachpixel of the result image (e.g., result image 113). Convolution can usea pixel matrix (e.g., 3×3 pixel matrix) to create a result image. Forexample, a source pixel in the selected area 103 and its surroundingpixels, within a 3×3 pixel matrix of the source pixel, can bemathematically merged to produce a single destination pixel in theblurred image 113. The pixel matrix can slide across the surface of theselected area 103 to produce destination pixels for the result image.Traditional solutions generally use the pixels of the selected area 103as the input to a blur filter. For example, traditionally, the pixelmatrix slides across the pixels of the selected area 103.

When a user selects multiple areas (e.g., selected area 103, selectedarea 105) in a source image 100 to blur, traditional solutions usuallyperform multiple blur operations. Traditional solutions perform multipleblur operation by executing a blur operation for each selected area.That is, traditional blurring solutions typically perform a first bluroperation to convolute 107 the pixels in the selected area 103 to createa corresponding result image (blurred image 113), and perform a secondblur operation to convolute 109 the pixels in the selected area 105 tocreate a corresponding result image (blurred image 115). Withconventional blurring solutions, the result image (e.g., result image113,115) that corresponds to a selected area is typically the same shapeand size as the selected area (e.g., area 103,105) since the input tothe blur filter is the pixels in the corresponding selected area.Traditional blurring solutions then separately place 117,119 theindividual result images (e.g., blurred images 113,115) over the sourceimage 100 to add a blur effect to the source image 100.

The user may subsequently select another area 121 to blur from thesource image 100. The other selected area 121 may have sub-areas thatoverlap with the previously selected areas 103,105. Since traditionalblurring solutions generally use the pixels in the selected area (e.g.,selected area 121) as the input to a blur filter, traditional blurringsolutions would typically perform another blur operation using thepixels of the other selected area 121 as input to create a separateresult image (e.g., blurred image 118) for the other selected area 121,and then place the blurred image 118 over the source image 100. That is,even though some of the selected areas 103,105,121 contain commonsub-areas, and some of the input (e.g., pixels) is common to the variousblur operations, conventional blurring solutions perform a separate bluroperation for each selected area 103, 105, 121, resulting in multipleand redundant operations for the common pixel input.

Implementations of the present disclosure can minimize the number ofoperations to be performed when blurring multiple selected areas of asource image. Unlike traditional blurring solutions that generally usethe pixels of a selected area (e.g., selected area 103) as the input toa blur filter, implementations of the present disclosure use a definedarea, which is larger than the selected area, as input to a blur filter.FIG. 1B illustrates a blur effect on an example image using a definedarea, which encompasses one or more selected areas and is larger thanthe selected area(s), as the input to a blur filter, in accordance withsome implementations.

Referring to FIG. 1B, source image 150 may be, for example, a digitalversion of a company's financial report, and the selected areas 153,155may have custom shapes (e.g., irregular shapes, pre-defined shapes) thatinclude financial data which the user wishes to conceal with a blureffect. A user may select multiple areas (e.g., area 153, area 155) toblur in source image 150.

Implementations of the present disclosure can perform a single bluroperation to create a single result image corresponding to the multipleseparate sub-areas (e.g., area 153, area 155). Performing the singleblur operation to create the single result image minimizes a number ofblur operations being performed relative to applying a same blur filterto the multiple separate sub-areas. Implementations of the presentdisclosure define an area that is large enough to encompass the selectedareas 153,155 and use the defined area as input to a blur filter. Thedefined area can be part of the source image 150, such as area 159, orthe defined area can be the area 165 of the entire source image 150.

Unlike traditional solutions, which would generally perform two bluroperations using the multiple selected areas as separate input to createseparate result images for the separate selected areas 153,155,implementations of the present disclosure reduce the number of bluroperations to perform by executing a single blur operation to blur thedefined area, which encompasses the selected areas 153,155, to create asingle result image. For example, if the defined area is area 159,implementations of the present disclosure can perform a single bluroperation to create a corresponding single result image 169 and canstore the result image 169 in a data store 110. In another example, ifthe defined area is area 165, implementations of the present disclosurecan perform a single blur operation to create a corresponding singleresult image 173 and can store the result image 173 in the data store110. Implementations of the present disclosure can use and re-use asingle result image (e.g., result image 169 or result image 173) to addthe blur effect for various selected areas (e.g., selected areas153,155) of the same source image.

Implementations of the present disclosure can receive multiple requeststo edit (e.g., blur) the same source image 150 and can use a resultimage that is stored in the data store 110 to respond to the requests.The requests may be subsequent requests, and may be from the same userand/or different users. For example, a user may subsequently selectanother area 167 to blur from the source image 150. Unlike conventionalsolutions, which would typically use the pixels of selected area 167 toperform an additional blur operation to create a corresponding resultimage for the selected area 167, implementations of the presentdisclosure can reuse a result image (e.g., result image 169 or resultimage 173) that is already stored in the data store 110, as will bediscussed in greater detail below in conjunction with FIG. 5. By usingan already existing result image from the data store 110 to respond tomultiple image editing requests, implementations of the presentdisclosure eliminate redundant operations, which would typically beperformed by conventional image editing solutions.

FIG. 2 illustrates example system architecture 200 in which aspects ofthe present disclosure can be implemented. The system architecture 200can include one or more user devices 203,205, one or more servermachines 240, and one or more data stores 250 coupled to each other overone or more networks 220. The one or more networks 220 can include oneor more public networks (e.g., the Internet), one or more privatenetworks (e.g., a local area network (LAN) or one or more wide areanetworks (WAN)), one or more wired networks (e.g., Ethernet network),one or more wireless networks (e.g., an 802.11 network or a Wi-Finetwork), one or more cellular networks (e.g., a Long Term Evolution(LTE) network), routers, hubs, switches, server computers, and/or acombination thereof. In one implementation, some components ofarchitecture 200 are not directly connected to each other. In oneimplementation, architecture 200 includes separate networks 220. Themachines 240 can be a rackmount server computer, a router computer, apersonal computer, a portable digital assistant, a mobile phone, alaptop computer, a tablet computer, a camera, a video camera, a netbook,a desktop computer, a media center, or any combination of the above.

The data stores 250 can store media items, such as, and not limited to,digital images, digital photos, digital documents, digital video,digital movies, digital music, mobile application content, websitecontent, social media updates, electronic books (ebooks), electronicmagazines, digital newspapers, digital audio books, electronic journals,web blogs, real simple syndication (RSS) feeds, electronic comic books,software applications, etc. For brevity and simplicity, a digital imageand image are used as an example of a media item throughout thisdocument. A data store can be a persistent storage that is capable ofstoring data. A persistent storage can be a local storage unit or aremote storage unit. Persistent storage can be a magnetic storage unit,optical storage unit, solid state storage unit, electronic storage units(main memory), or similar storage unit. Persistent storage can be amonolithic device or a distributed set of devices. A ‘set’, as usedherein, refers to any positive whole number of items. In someimplementations of the present disclosure, a data store 250 is anetwork-attached file server. In some implementations of the presentdisclosure, a data store 250 is some other type of persistent storage,such as an object-oriented database, a relational database, and soforth.

The user devices 203,205 can be portable computing devices such as acellular telephones, personal digital assistants (PDAs), portable mediaplayers, netbooks, laptop computers, an electronic book reader or atablet computer (e.g., that includes a book reader application), aset-top box, a gaming console, a television, and the like. The userdevices 203,205 can include one or more data stores 260A,B to storedata.

Users 210A,B can access data that is stored in the data store 250 viauser devices 203,205. The user devices 203,205 can run an operatingsystem (OS) that manages hardware and software of the user devices203,205. A media application 207 can run on the user devices 203,205(e.g., on the OS of the user devices). For example, the mediaapplication 207 may be a web browser that can access content served byan application server (e.g., server machine 240). In another example,the media application 207 may be a mobile application (e.g., an app)that can access content served by a mobile application server (e.g.,server machine 240).

The server machine 240 can provide web applications and/or mobile deviceapplications and data for the user devices 203, 205. One or more datastores 250 can include data to be protected from data loss, such as datathat is sensitive and/or confidential. Data in the data stores 250 canbe classified as data to be protected, for example, based onconfiguration parameters stored in the data store 250 and/or specifiedby a user (e.g., system administrator).

The user devices 203,205 can include an image editing module 215 to editan image. Examples of editing an image can include, and are not limitedto, blurring an image, applying a color filter to an image, and adding awatermark to an image. For example, a user 210A may generate a dashboardimage using user device 203 and data that is stored in data store 250.

FIG. 3 illustrates an example of creating a blur effect for customshapes on an example dashboard image 300, in accordance with variousimplementations. In information management, a dashboard is a userinterface (e.g., graphical user interface) showing a graphicalpresentation of the current status (snapshot) and trends of performanceto enable informed decisions to be made at a glance. An image of thedashboard can be created. The dashboard image 300 can includeconfidential data 305,307,309. The dashboard image 300 can be generatedusing a late binding schema and at a particular moment in time. A latebinding schema is described in greater detail below. A user 210A maywish to share the dashboard image 300 with another user 210B, but maywish to conceal the confidential data 305,307,309 when sharing thedashboard image 300. The user 210A can provide input specifying selectedareas 353,355,357 in the source image (e.g., dashboard image 300). Theimage editing module 215 can use the input to define an area thatencompasses the selected areas 353,355,357 and use the defined area tocreate, for example, a blur effect for the selected areas 353,355,357 toconceal the confidential data 305,307,309 in the dashboard image 300 forsharing with user 210B.

Unlike traditional editing solutions, the image editing module 215 canedit (e.g., blur) multiple areas of an image by applying a filter to themultiple areas of the image in a single process. A “single process”hereinafter refers to executing a single filter operation, such asexecuting a single blur operation with respect to multiple areasselected by a user for blurring. As described above, a blur operationrefers to calculating weighted averages of pixels in an area in thesource image to create each pixel of a result image. FIG. 4 illustratesan example of creating a blur effect for multiple custom shapes on anexample source image 400 by executing a single blur operation, inaccordance with various implementations. The source image 400 may be adashboard image (e.g., dashboard image 300 in FIG. 3), a digital photoof a group of people, or any other image. The image editing module 215can receive input identifying multiple, separately selected areas403,405,407. The image editing module 215 can define an area 409 in thesource image 400 that includes the selected areas 403,405,407 and cancreate a single corresponding result image 411. For example, the definedarea 409 can be the area of the entire source image 400, and the imageediting module 215 can use the pixels of the defined area 409 as inputto a blur filter (e.g., Gaussian filter, motion filter, box filter, spinfilter, zoom filter) to produce the result image 411. The result image411 is a blur of the defined area 409. The image editing module 215performs a single blur operation on the defined area 409 to create thesingle result image 411. The image editing module 215 can define thearea 409 based on user input and/or configuration parameters that arestored in a data store (e.g., data stores 250,260A,B in FIG. 2).

To create the blur effect for the selected areas 403,405,407, the imageediting module 215 transforms the source image 400 into a transformedimage 412 by changing the selected areas 403,405,407 in the source image400 into transparent areas 413,415,417. The image editing module 215aligns portions of the result image 411 with the transparent areas413,415,417 in the transformed image 412. In one implementation, theimage editing module 215 places the transformed image 412 containing thetransparent areas 413,415,417 in the foreground 435 and places theresult image 411 in the background 431. The image editing module 215creates 437 an alignment such that the transparent areas 413,415,417 andthe portions 421,423,425 of the result image 411 that correspond to thetransparent areas 413,415,417 are in the same position. The portions421,423,425 of the result image 411 that correspond to the transparentareas 413,415,417 in the transformed image 412 are revealed through thetransparent areas 413,415,417 to create an edited image 450. That is,the portions 421,423,425 of the result image 411 that correspond to thetransparent areas 413,415,417 include the blur effect and are revealedvia the corresponding transparent areas 413,415,417. The edited image450 can be stored in a data store and can be shared, for example, withanother user 210B. The edited image 450 can include a foreground layercontaining the transparent areas 413,415,417 and a background layercontaining the portions 421,423,425 of the result image 411 thatcorrespond to the transparent areas 413,415,417.

Returning to FIG. 2, the edited image (e.g., edited image 450 in FIG. 4)can be stored in data store 260A, data store 260B, and/or data store250. The image editing module 215 can also store the result image (e.g.,result image 411 in FIG. 4) in a data store (e.g., data stores250,260A,B). In one implementation, the image editing module 215 residesin a user device 203,205, and the result image is stored in acorresponding data store (e.g., data store 260A in user device 203, datastore 260B in user device 205). In another implementation, the imageediting module 215 resides in a server machine 240, and the result imageis stored in a data store (e.g., data store 250) that is coupled to theserver machine 240. The user (e.g., user 210A or user 210B) maysubsequently select another area from the same source image and requestthat the other selected area be blurred. The image editing module 215can use the result image (e.g., result image 411 in FIG. 4) that isstored in a data store (e.g., data store 260A, data store 260B, datastore 250) to respond to the request.

FIG. 5 illustrates an example of minimizing the number of editing (e.g.,blurring) operations for responding to multiple requests to edit thesame source image with the same filter, in accordance with variousimplementations. A user 501 can use a user device 502 to select a sourceimage 507 and one or more areas (e.g., selected area 513) to blur in thesource image 507. The source image 507 can be stored in a data store ofthe user device 502. The image editing module 215 can define an area(e.g., entire source image 507 area) that encompasses the selected area513 and can convolute the defined area using the pixels of the definedarea as input to a blur filter (e.g., Gaussian filter, motion filter,box filter, spin filter, zoom filter) to create a result image 509. Theimage editing module 215 can store the result image 509, whichcorresponds to the source image 507 and the selected area 513, in a datastore 511. In one implementation, the data store 511 is a data storethat is local to the user device 502. Alternatively, the data store 511is a centralized data store accessible to user devices 502,504, forexample, via one or more networks. In one implementation, the imageediting module 215 on the user device 502 sends the result image 509 toa server machine (e.g., server machine 240 in FIG. 2) via the one ormore networks, and the server machine stores the result image 509 in thedata store 511. In one implementation, the result image 509 is stored onone or more data stores of one or more user devices and a centralizeddata store.

In one implementation, the user device 502 sends the source image 507and input data describing the selected area 513 to the server machine,and the server machine creates the result image 509 and stores theresult image 509 in the data store 511. The server machine can send theresult image 511 to the image editing module 215 on the user devices502,504.

The image editing module 215 on the user device 502 can create atransformed image 508 that corresponds to the source image 507. Thetransformed image 508 can include a transparent area 515 thatcorresponds to the selected area 513. The image editing module 215 canplace the transformed image 508 in the foreground and the result image509 in the background. The image editing module 215 can align a portionof the result image 509 that corresponds to the transparent area 515 inthe transformed image 508 to be in the same position as the transparentarea 515 to create an edited image 517. The edited image 517 reveals theblurred portion of the result image 509 that corresponds to thetransparent area 515 in the transformed image through the transparentarea 515.

There may be another request to use the same blur filter to add a blureffect to the same source image 507. For example, the request may befrom another user device 504. In another example, the request may befrom the same user device 502. The user can be a different user (e.g.,user 503) or the same user (e.g., user 501). The request may be for adifferent selected area 521 of the same source image 507. The imageediting module 215, for example, on the user device 504 can receive userinput of the selected area 521 to blur. In one implementation, the imageediting module 215 on the user device 504 determines whether a localdata store on the user device 504 stores a result image that correspondsto the source image. In another implementation, the user device 504sends a request to the server machine for a result image (e.g., resultimage 509), which may be stored in a centralized data store, thatcorresponds to the source image 507. In one implementation, the imageediting module can determine whether there is a corresponding resultimage in a local data, and if not, send a request to a server machinefor a corresponding result image.

If there is not a corresponding result image in a data store (e.g.,local data store, centralized data store), the image editing module onthe user device 504 or the image editing module on a server machine, cancreate a result image for the source image 507. If there is acorresponding result image in the data store (e.g., data store 511), theuser device 504 can receive the result image 509 for example, from theserver machine, and can avoid performing any blur operations by reusingthe result image 509 from the data store 511, thus reducing the amountof resources (e.g., central processing unit (CPU) time and memory) usedto create the blur effect. The image editing module 215 can create atransformed image 513 that includes the source image 507 and atransparent area 523 that corresponds to the selected area 521. The userdevice 504 can place the transformed image 513 in the foreground and theresult image 509 in the background. The user device 504 can align aportion of the result image 509, which corresponds to the transparentarea 523 in the transformed image 513, to be in the same position as thetransparent area 523 to create an edited image 525. The edited image 525reveals the blurred portion of the result image 509 that corresponds tothe transparent area 523 in the transformed image 513 through thetransparent area 523.

FIG. 6 is a flow diagram of an implementation of a method 600 forediting (e.g., blurring) an image. The method may be performed byprocessing logic that may comprise hardware (circuitry, dedicated logic,etc.), software (such as is run on a general purpose computer system ora dedicated machine), or a combination of both. In one implementation,the method 600 is performed by the image editing module 215 on a userdevice 203,205 in FIG. 2. In another implementation, one or moreportions of method 600 are performed by an image editing module 215 on aserver machine (e.g., server machine 240 in FIG. 2) coupled to the userdevice over one or more networks.

At block 601, the image editing module receives input representing aselection of one or more areas of a source image. The image editingmodule can receive a request, for example, to blur a source image, andthe request can include the input specifying the selected area(s) to beblurred. The selected area(s) can include one or more separate sub-areasof the source image. The selected area(s) can include one or more areasof random or irregular shape(s) and/or pre-defined shape(s). Sometraditional blurring solutions limit the selected area(s) of a sourceimage to a pre-defined shape such as a rectangle, a square, a circle, anellipse, or a triangle. For example, a conventional blurring solutionmay provide a user with a list of shapes to use as a blurring tool.Implementations of the present disclosure can blur areas of a sourceimage that are not limited to a pre-defined shape.

The input specifying the selected area(s) can be user input received viaa user interface (e.g., graphical user interface) and/or an inputdevice. For example, a user may provide input using a touch screen of auser device to select area(s) in the source image. The input can be inthe form of x-y coordinates for the various points in the selectedarea(s). The one or more selected areas can include data to be protectedand/or data derived from one or more data stores. The data can bederived using a late binding schema as described in greater detailbelow. For example, a dashboard image can be generated using dataextracted via a late binding schema. The dashboard image can begenerated at a particular point in time and can include datacorresponding to the particular point in time. The user input canspecify a selection of the data to be blurred in the dashboard image.

At block 603, the image editing module creates a result image thatcorresponds to the selected area(s). The result image can be a blur of apart of the source image or a blur of the entire image. That is, theresult image corresponds to a portion of the source image that containsthe selected area(s). The portion of the image has a size that isgreater than an aggregate size of the selected area(s). The selectedarea(s) have an aggregate size that is less than the size of the blurredarea. The blurred area corresponds to a blurring of a single portion ofthe result image that contains the multiple separate selected areas inthe source image. For example, the result image is a blurred image thathas a size of the source image and the one or more selected areas have atotal size that is less than the size of the source image. To create theresult image (e.g., blurred image), the image editing module defines anarea of the source image that encompasses the selected area(s) andconvolutes the defined area with a filter (e.g., Gaussian filter, motionfilter, box filter, spin filter, zoom filter) to produce a single resultimage for the selected area(s). Unlike conventional solutions, which usethe pixels of separate selected area(s) as separate inputs to performmultiple blur operations and create multiple result images for multipleselected areas, the image editing module uses the pixels of the definedarea as input to a blur filter to execute a single blur operation on thedefined area to create a single result image that corresponds to themultiple selected areas.

The image editing module can define the area of the source image thatencompasses the selected area(s) based on configuration parameters thatare stored in a data store. For example, the selected area(s) of thesource image may be a portion of the source image and the configurationparameters may indicate that the defined area should be an area thatcovers the selected area(s) and is 25% larger than the area covering theselected area(s). In another example, the selected area(s) of the sourceimage may be a small portion of the source image and the configurationparameters may indicate that the defined area should be the area of theentire source image. The image editing module can store the result image(e.g., result image 169, result image 173 in FIG. 1, result image 411 inFIG. 4) in a data store (e.g., data store 110 in FIG. 1).

At block 605, the image editing module replaces the selected area(s) inthe source image with corresponding portion(s) of the result image tocreate an edited image (e.g., edited image 450 in FIG. 4). The editedimage includes the blur effect. In one implementation, the image editingmodule replaces the selected area(s) in the source image with thecorresponding portion(s) of the result image by identifying theportion(s) corresponding to the selected area(s) in the result image(e.g., based on the x-y coordinates), transforming the result image(excluding the identified portion(s)) to make the remaining part of theresult image transparent, and then applying the transformed result imageto the source image, as will be discussed in more detail below inconjunction with FIG. 8. In another implementation, the image editingmodule replaces the selected area(s) in the source image with thecorresponding portion(s) of the result image by transforming the sourceimage to make the selected portion(s) of the source image (but not theremaining part of the result image) transparent, and then combining thetransformed source image with the result image, as will be described ingreater detail below in conjunction with FIG. 7.

At block 607, the image editing module provides the edited image havingthe selected area(s) replaced with the corresponding portion(s) of theresult image. For example, the image editing module can provide theedited image, which includes the blur effect, to a user, and the usercan share the edited image, for example, with another user. The selectedarea(s) can correspond to data to be protected and the edited image canconceal the data to be protected. In one implementation, the imageediting module receives a request to blur a different selected area ofthe same source image. The different selected area can include asub-area that is not in included in any previously selected area(s). Theimage editing module can respond to the request by replacing thedifferent selected area of the source image with a corresponding portionof the result image.

FIG. 7 is a flow diagram of an implementation of a method 700 forreplacing one or more selected area(s) with a corresponding portion of aresult image. The method may be performed by processing logic that maycomprise hardware (circuitry, dedicated logic, etc.), software (such asis run on a general purpose computer system or a dedicated machine), ora combination of both. In one implementation, the method 700 isperformed by the image editing module 215 on a user device 203,205 inFIG. 2. In another implementation, one or more portions of method 700are performed by a server machine (e.g., server machine 240 in FIG. 2)coupled to the user device over one or more networks.

At block 701, the image editing module receives a request to blur asource image. At block 703, the image editing module receives input ofone or more selected areas in the source image. For example, the inputcan be user input received via an input device (e.g., touch screen). Inanother example, the image editing module resides in a server machineand receives user input that is transmitted by a user device to theserver machine via a network.

At block 705, the image editing module identifies a result image thatcorresponds to a source image. For example, the result image can bestored in a data store. The data store can include data that associatesone or more result images with one or more source images, and viceversa. In one implementation, a data store stores multiple result imagesthat correspond to the same source image. For example, as shown in FIG.1, a data store 100 may store result image 169 and result image 173,both of which may be associated with source image 150.

Referring again to FIG. 7, at block 705, in one implementation, theimage editing module determines whether the identified result imageencompasses the selected area(s). For example, result image 169 may notinclude the selected area(s). If the identified result image does notcontain the selected area(s), the image editing module selects adifferent result image in the data store and determines whether thedifferent result image encompasses the selected area(s). In oneimplementation, if there is not a result image, which encompasses theselected area(s), in the data store, the image editing module creates aresult image that encompasses the selected area(s), as was discussed inmore detail above.

At block 707, the image editing module aligns the selected area(s) ofthe source image with a corresponding portion of the result image. Thesource image can be in the foreground, and the result image can be inthe background. At block 709, the image editing module transforms theselected area(s) in the source image in a foreground layer intotransparent area(s). Transforming the selected area(s) into transparentarea(s) reveals the corresponding blurred portion in the result image ina background layer through the transparent area(s). The image editingmodule can superimpose the source image with the transparent areas ontop of the result image in the background layer.

FIG. 8 is a flow diagram of an implementation of a method 800 forreplacing one or more selected areas with a corresponding portion of aresult image. The method may be performed by processing logic that maycomprise hardware (circuitry, dedicated logic, etc.), software (such asis run on a general purpose computer system or a dedicated machine), ora combination of both. In one implementation, the method 800 isperformed by the image editing module 215 on a user device 203,205 inFIG. 2. In another implementation, one or more portions of method 800are performed by a server machine (e.g., server machine 240 in FIG. 2)coupled to the user device over one or more networks.

At block 801, the image editing module receives a request to blur asource image. At block 803, the image editing module receives input ofone or more selected areas in the source image. For example, the inputcan be user input received via an input device (e.g., touch screen). Inanother example, the image editing module resides in a server machineand receives user input that is transmitted by a user device to theserver machine via a network.

At block 805, the image editing module identifies a result image thatcorresponds to the source image. In one implementation, the imageediting module determines whether the identified result imageencompasses the selected area(s). If the identified result image doesnot contain the selected area(s), the image editing module selects adifferent result image in the data store and determines whether thedifferent result image encompasses the selected area(s). In oneimplementation, if there is not a result image, which encompasses theselected area(s), in the data store, the image editing module creates aresult image that encompasses the selected area(s).

At block 807, the image editing module aligns the selected area(s) ofthe source image with a corresponding portion of the result image. Theresult image can be in the foreground and the source image can be in thebackground. At block 809, the image editing module transforms the resultimage in the foreground, excluding the portion(s) that correspond to theselected area(s), into a transparent area. Transforming the result imagein the foreground reveals the corresponding portion in the source image.The image editing module can superimpose the transformed result image ontop of the source image.

FIG. 9 is an example of replacing one or more selected areas with acorresponding portion of a result image, according to variousimplementations. FIG. 9 illustrates an example of the image editingmodule performing method 800 of FIG. 8. The image editing module 215 canreceive input specifying one or more selected areas 901 of a sourceimage 900. In this example, the selected area(s) 901 include anirregular shape. The image editing module 215 can define an area 903 inthe source image 900 that includes the selected area(s) 901. The imageediting module 215 can define the area 903 to also be larger than thecombined selected area(s).

The image editing module 215 can create a result image 905 thatcorresponds to the defined area 903. For example, the defined area 903is the area of the entire source image 900, and the image editing module215 can convolute the defined area 903 with a blur filter (e.g.,Gaussian filter, motion filter, box filter, spin filter, zoom filter) toproduce the result image 905 for the defined area 903. The image editingmodule 215 performs a single blur operation to create the result image905.

To create the blur effect for the selected area(s) 901, the imageediting module 215 transforms the result image 905 to include atransparent portion 907 and blurred portion(s). 909. The blurredportion(s) 909 correspond to selected area(s) 901, and the transparentportion 907 is the remaining part of the result image, excluding theblurred portion 909. The blurred portion 909 is an irregular shape, andthe image editing module can use one or more pre-defined shapes tocreate the irregular shape for the blurred portion 909.

The image editing module 215 aligns 916 the transformed image 911 withthe source image 900. In one implementation, the image editing module215 places the transformed image 911 in the foreground 913 and placesthe source image 900 in the background 915. The image editing module 215creates an alignment such that the blurred portion(s) 909 and theselected area(s) 901 are in the same position(s). The transformed image911 is in the foreground, and the portion of the source image 915 thatcorresponds to the transparent portion 907 is revealed through thetransparent portion 907 to create an edited image 950. The edited image950 can be stored in a data store and can be shared, for example, withanother user.

In one implementation, the selected area(s) of an image (e.g., sourceimage) includes data derived using a late binding schema. For example,an image (e.g., dashboard image) can be generated at a particular momentin time and using a late binding schema. Implementations of the presentdisclosure can process real-time data. For example, the dashboard imagecan include real-time performance data that corresponds to theparticular moment in time. Implementations of the present disclosure caninclude an event-based system, such as the SPLUNK® ENTERPRISE systemproduced by Splunk Inc. of San Francisco, Calif., to store and processperformance data. The SPLUNK® ENTERPRISE system is the leading platformfor providing real-time operational intelligence that enablesorganizations to collect, index, and harness machine-generated data fromvarious websites, applications, servers, networks, and mobile devicesthat power their businesses. The SPLUNK® ENTERPRISE system isparticularly useful for analyzing unstructured performance data, whichis commonly found in system log files. Although many of the techniquesdescribed can include the SPLUNK® ENTERPRISE system, the techniques arealso applicable to other types of data server systems.

In the SPLUNK® ENTERPRISE system, performance data is stored as“events,” wherein each event comprises a collection of performance dataand/or diagnostic information that is generated by a computer system andis correlated with a specific point in time. Events can be derived from“time series data,” wherein time series data comprises a sequence ofdata points (e.g., performance measurements from a computer system) thatare associated with successive points in time and are typically spacedat uniform time intervals. Events can also be derived from “structured”or “unstructured” data. Structured data has a predefined format, whereinspecific data items with specific data formats reside at predefinedlocations in the data. For example, structured data can include dataitems stored in database fields or data items stored in fields in a datastructure defined by a computer program. In contrast, unstructured datadoes not have a predefined format. This means that unstructured data cancomprise various data items having different data types that can resideat different locations. For example, when the data source is anoperating system log, an event can include one or more lines from theoperating system log containing raw data that includes different typesof performance and diagnostic information associated with a specificpoint in time. Examples of data sources from which an event may bederived include, but are not limited to: web servers; applicationservers; databases; firewalls; routers; operating systems; and softwareapplications that execute on computer systems, mobile devices, andsensors. The data generated by such data sources can be produced invarious forms including, for example and without limitation, server logfiles, activity log files, configuration files, messages, network packetdata, performance measurements and sensor measurements. An eventtypically includes a timestamp that may be derived from the raw data inthe event, or may be determined through interpolation between temporallyproximate events having known timestamps.

The SPLUNK® ENTERPRISE system also facilitates using a flexible schemato specify how to extract information from the event data, wherein theflexible schema may be developed and redefined as needed. Note that aflexible schema may be applied to event data “on the fly,” when it isneeded (e.g., at search time), rather than at ingestion time of the dataas in traditional database systems. Because the schema is not applied toevent data until it is needed (e.g., at search time), it is referred toas a “late-binding schema.”

During operation, the SPLUNK® ENTERPRISE system starts with raw data,which can include unstructured data, machine data, performancemeasurements or other time-series data, such as data obtained from weblogs, syslogs, or sensor readings. It divides this raw data into“portions,” and optionally transforms the data to produce time stampedevents. The system stores the time stamped events in a data store, andenables a user to run queries against the data store to retrieve eventsthat meet specified criteria, such as containing certain keywords orhaving specific values in defined fields. Note that the term “field”refers to a location in the event data containing a value for a specificdata item.

As noted above, the SPLUNK® ENTERPRISE system facilitates using alate-binding schema while performing queries on events. A late-bindingschema specifies “extraction rules” that are applied to data in theevents to extract values for specific fields. More specifically, theextraction rules for a field can include one or more instructions thatspecify how to extract a value for the field from the event data. Anextraction rule can generally include any type of instruction forextracting values from data in events. In some cases, an extraction rulecomprises a regular expression, in which case the rule is referred to asa “regex rule.”

In contrast to a conventional schema for a database system, alate-binding schema is not defined at data ingestion time. Instead, thelate-binding schema can be developed on an ongoing basis until the timea query is actually executed. This means that extraction rules for thefields in a query may be provided in the query itself, or may be locatedduring execution of the query. Hence, as an analyst learns more aboutthe data in the events, the analyst can continue to refine thelate-binding schema by adding new fields, deleting fields, or changingthe field extraction rules until the next time the schema is used by aquery. Because the SPLUNK® ENTERPRISE system maintains the underlyingraw data and provides a late-binding schema for searching the raw data,it enables an analyst to investigate questions that arise as the analystlearns more about the events.

In the SPLUNK® ENTERPRISE system, a field extractor may be configured toautomatically generate extraction rules for certain fields in the eventswhen the events are being created, indexed, or stored, or possibly at alater time. Alternatively, a user may manually define extraction rulesfor fields using a variety of techniques.

Also, a number of “default fields” that specify metadata about theevents rather than data in the events themselves can be createdautomatically. For example, such default fields can specify: a timestampfor the event data; a host from which the event data originated; asource of the event data; and a source type for the event data. Thesedefault fields may be determined automatically when the events arecreated, indexed or stored.

In some embodiments, a “tag” may be assigned to two or more fields thatcontain equivalent data items, even though the fields are associatedwith different events and possibly different extraction rules. Byenabling a single tag (e.g., a field name) to be used to identifyequivalent fields from different types of events generated by differentdata sources, the system facilitates use of a “common information model”(CIM) across the different data sources.

FIG. 10 illustrates a diagram of a machine in an example form of acomputer system 1000 within which a set of instructions, for causing themachine to perform any one or more of the methodologies discussedherein, may be executed. The computer system 1000 can be user device203,205 in FIG. 2. The computer system 1000 can be server machine 240 inFIG. 2. In alternative implementations, the machine may be connected(e.g., networked) to other machines in a LAN, an intranet, an extranet,or the Internet. The machine may operate in the capacity of a server ora client machine in client-server network environment, or as a peermachine in a peer-to-peer (or distributed) network environment. Themachine may be a personal computer (PC), a tablet PC, a set-top box(STB), a Personal Digital Assistant (PDA), a cellular telephone, a webappliance, a server, a network router, switch or bridge, or any machinecapable of executing a set of instructions (sequential or otherwise)that specify actions to be taken by that machine. Further, while only asingle machine is illustrated, the term “machine” shall also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions to perform any one or more ofthe methodologies discussed herein.

The example computer system 1000 includes a processing device(processor) 1002, a main memory 1004 (e.g., read-only memory (ROM),flash memory, dynamic random access memory (DRAM) such as synchronousDRAM (SDRAM), double data rate (DDR SDRAM), or DRAM (RDRAM), etc.), astatic memory 1006 (e.g., flash memory, static random access memory(SRAM), etc.), and a data storage device 1018, which communicate witheach other via a bus 1030.

Processor 1002 represents one or more general-purpose processing devicessuch as a microprocessor, central processing unit, or the like. Moreparticularly, the processor 1002 may be a complex instruction setcomputing (CISC) microprocessor, reduced instruction set computing(RISC) microprocessor, very long instruction word (VLIW) microprocessor,or a processor implementing other instruction sets or processorsimplementing a combination of instruction sets. The processor 1002 mayalso be one or more special-purpose processing devices such as anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), a digital signal processor (DSP), network processor,or the like. The processor 1002 is configured to execute instructions1022 for performing the operations and steps discussed herein.

The computer system 1000 may further include a network interface device1008. The computer system 1000 also may include a video display unit1010 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)),an input device 1012 (e.g., a keyboard, and alphanumeric keyboard, amotion sensing input device, touch screen), a cursor control device 1014(e.g., a mouse), and a signal generation device 1016 (e.g., a speaker).

The data storage device 1018 can include a computer-readable storagemedium 1028 on which is stored one or more sets of instructions 1022(e.g., software) embodying any one or more of the methodologies orfunctions described herein. The instructions 1022 can also reside,completely or at least partially, within the main memory 1004 and/orwithin the processor 1002 during execution thereof by the computersystem 1000, the main memory 1004 and the processor 1002 alsoconstituting computer-readable storage media. The instructions 1022 mayfurther be transmitted or received over a network 1020 via the networkinterface device 1008.

In one implementation, the instructions 1022 include instructions for animage editing module (e.g., an image editing module 215 in FIG. 2)and/or a software library containing methods that call the image editingmodule. While the computer-readable storage medium 1028(machine-readable storage medium) is shown in an exemplaryimplementation to be a single medium, the term “computer-readablestorage medium” should be taken to include a single medium or multiplemedia (e.g., a centralized or distributed database, and/or associatedcaches and servers) that store the one or more sets of instructions. Theterm “computer-readable storage medium” shall also be taken to includeany medium that is capable of storing, encoding or carrying a set ofinstructions for execution by the machine and that cause the machine toperform any one or more of the methodologies of the present disclosure.The term “computer-readable storage medium” shall accordingly be takento include, but not be limited to, solid-state memories, optical media,and magnetic media.

In the foregoing description, numerous details are set forth. It will beapparent, however, to one of ordinary skill in the art having thebenefit of this disclosure, that the present disclosure may be practicedwithout these specific details. In some instances, well-known structuresand devices are shown in block diagram form, rather than in detail, inorder to avoid obscuring the present disclosure.

Some portions of the detailed description have been presented in termsof algorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of steps leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It hasproven convenient at times, for reasons of common usage, to refer tothese signals as bits, values, elements, symbols, characters, terms,numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the following discussion,it is appreciated that throughout the description, discussions utilizingterms such as “receiving”, “creating”, “replacing”, “determining”,“performing”, “defining”, “executing”, “generating”, “transforming”,“superimposing”, “creating”, “sharing”, or the like, refer to theactions and processes of a computer system, or similar electroniccomputing device, that manipulates and transforms data represented asphysical (e.g., electronic) quantities within the computer system'sregisters and memories into other data similarly represented as physicalquantities within the computer system memories or registers or othersuch information storage, transmission or display devices.

For simplicity of explanation, the methods are depicted and describedherein as a series of acts. However, acts in accordance with thisdisclosure can occur in various orders and/or concurrently, and withother acts not presented and described herein. Furthermore, not allillustrated acts may be required to implement the methods in accordancewith the disclosed subject matter. In addition, those skilled in the artwill understand and appreciate that the methods could alternatively berepresented as a series of interrelated states via a state diagram orevents. Additionally, it should be appreciated that the methodsdisclosed in this specification are capable of being stored on anarticle of manufacture to facilitate transporting and transferring suchmethods to computing devices. The term article of manufacture, as usedherein, is intended to encompass a computer program accessible from anycomputer-readable device or storage media.

Certain implementations of the present disclosure also relate to anapparatus for performing the operations herein. This apparatus may beconstructed for the intended purposes, or it may comprise a generalpurpose computer selectively activated or reconfigured by a computerprogram stored in the computer. Such a computer program may be stored ina computer readable storage medium, such as, but not limited to, anytype of disk including floppy disks, optical disks, CD-ROMs, andmagnetic-optical disks, read-only memories (ROMs), random accessmemories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any typeof media suitable for storing electronic instructions.

Reference throughout this specification to “one implementation” or “animplementation” means that a particular feature, structure, orcharacteristic described in connection with the implementation isincluded in at least one implementation. Thus, the appearances of thephrase “in one implementation” or “in an implementation” in variousplaces throughout this specification are not necessarily all referringto the same implementation. In addition, the term “or” is intended tomean an inclusive “or” rather than an exclusive “or.” Moreover, thewords “example” or “exemplary” are used herein to mean serving as anexample, instance, or illustration. Any aspect or design describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other aspects or designs. Rather, use of the words“example” or “exemplary” is intended to present concepts in a concretefashion.

It is to be understood that the above description is intended to beillustrative, and not restrictive. Many other implementations will beapparent to those of skill in the art upon reading and understanding theabove description. The scope of the disclosure should, therefore, bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

What is claimed is:
 1. A method, comprising: receiving, by a processingdevice, input representing a selection of a first area of an image;determining, by the processing device, whether the first selected areaof the image corresponds to a blurred area previously created for asecond selected area of the image, the blurred area previously createdfor the second selected area of the image having a size that is lessthan a size of the image; and responsive to determining that the firstselected area of the image corresponds to the blurred area previouslycreated for the second selected area of the image, replacing, by theprocessing device, the first selected area of the image with acorresponding portion of the blurred area previously created for thesecond selected area of the image.
 2. The method of claim 1, wherein theblurred area is created by: determining that the second selected areacomprise a plurality of separate sub-areas; and performing a single bluroperation to create a single result image corresponding to the pluralityof separate sub-areas, wherein performing the single blur operation tocreate the single result image minimizes a number of blur operationsbeing performed relative to applying a same blur filter to the pluralityof separate sub-areas.
 3. The method of claim 1, wherein the blurredarea is created by: defining an area of the image that encompasses thesecond selected area; and executing a single blur operation using pixelsin the defined area as input to a blur filter, the blur filter being oneof a Gaussian filter, a motion filter, a box filter, a spin filter, or azoom filter.
 4. The method of claim 1, wherein the first selected areaof the image comprises data to be protected, the data being derived froma data store.
 5. The method of claim 1, wherein the first selected areaof the image comprises data derived using a late binding schema.
 6. Themethod of claim 1, wherein the first selected area of the image comprisea plurality of separate areas in the image, and the blurred areacorresponds to a blurring of a single portion of the image that containsthe plurality of separate areas in the image.
 7. The method of claim 1,wherein the first selected area comprises an irregular shape.
 8. Themethod of claim 1, wherein receiving input of the first selected area ofthe image comprises: generating a dashboard image using data extractedvia a late binding schema, wherein the dashboard image is generated at aparticular point in time and comprises data corresponding to theparticular point in time; and receiving a selection of the data to beblurred in the dashboard image.
 9. The method of claim 1, whereinreplacing the first set of selected areas with the corresponding portionof the blurred area comprises: transforming the first selected area inthe image into transparent areas in a foreground layer; andsuperimposing the image with the transparent areas on top of the blurredarea, wherein the blurred area is in a background layer.
 10. The methodof claim 1, wherein replacing the first selected area with thecorresponding portion of the blurred area comprises: transforming theblurred area to include a transparent area, the transparent areaexcluding one or more portions of the blurred area that correspond tothe first selected area in the image; and superimposing the transformedblurred area on top of the image.
 11. The method of claim 1, furthercomprising: creating an edited image, the edited image comprising the efirst selected area replaced with the corresponding portion of theblurred area; and sharing the edited image, wherein the edited imageconceals data to be protected.
 12. The method of claim 1, furthercomprising: receiving a request to blur a different selected area of theimage, wherein the different selected area comprises a sub-area that isnot included in the first selected area; and replacing the differentselected area of the image with a second corresponding portion of theblurred area.
 13. A system comprising: a memory to store an image and ablurred area previously created for a first selected area of the image;and a processing device coupled with the memory to: receive inputrepresenting a selection of a second area of the image; determinewhether the second selected area of the image corresponds to the blurredarea previously created for the first selected area of the image, theblurred area previously created for the first selected area of the imagehaving a size that is less than a size of the image; and responsive todetermining that the second selected area of the image corresponds tothe blurred area previously created for the first selected area of theimage, replace the second selected area of the image with acorresponding portion of the blurred area previously created for thefirst selected area of the image.
 14. The system of claim 13, whereinthe blurred area is created by: determining that the first selected areacomprise a plurality of separate sub-areas; and performing a single bluroperation to create a single result image corresponding to the pluralityof separate sub-areas, wherein performing the single blur operation tocreate the single result image minimizes a number of blur operationsbeing performed relative to applying a same blur filter to the pluralityof separate sub-areas.
 15. The system of claim 13, wherein the secondselected area of the image comprises data to be protected, the databeing derived from a data store.
 16. The system of claim 13, wherein thesecond selected area of the image comprises data derived using a latebinding schema.
 17. The system of claim 13, wherein the first selectedarea of the image comprise a plurality of separate areas in the image,and the blurred area corresponds to a blurring of a single portion ofthe image that contains the first selected area in the image.
 18. Thesystem of claim 13, wherein the blurred area is created by: defining anarea of the image that encompasses the first selected area; andexecuting a single blur operation using pixels in the defined area asinput to a blur filter, the blur filter being one of a Gaussian filter,a motion filter, a box filter, a spin filter, or a zoom filter.
 19. Thesystem of claim 13, wherein to replace the second selected area with thecorresponding portion of the blurred area comprises: transforming thesecond selected area in the image into a transparent area in aforeground layer; and superimposing the image with the transparent areaon top of the blurred area, wherein the blurred area is in a backgroundlayer.
 20. The system of claim 13, wherein the processing device isfurther to: receive a request to blur a different selected area of theimage, wherein the different selected area comprises a sub-area that isnot included in the second selected area; and replace the differentselected area of the image with a second corresponding portion of theblurred area.
 21. A non-transitory computer readable storage mediumencoding instructions thereon that, in response to execution by aprocessing device, cause the processing device to perform operationscomprising: receiving, by the processing device, input representing aselection of a first area of an image; determining, by the processingdevice, whether the first selected area of the image corresponds to ablurred area previously created for a second selected area of the image,the blurred area previously created for the second selected area of theimage having a size that is less than a size of the image; andresponsive to determining that the first selected area of the imagecorresponds to the blurred area previously created for the secondselected area of the image, replacing, by the processing device, thefirst selected area of the image with a corresponding portion of theblurred area previously created for the second selected area of theimage.
 22. The non-transitory computer readable storage medium of claim21, wherein the blurred area is created by: determining that the secondselected area comprise a plurality of separate sub-areas; and performinga single blur operation to create a single result image corresponding tothe plurality of separate sub-areas, wherein performing the single bluroperation to create the single result image minimizes a number of bluroperations being performed relative to applying a same blur filter tothe plurality of separate sub-areas.
 23. The non-transitory computerreadable storage medium of claim 21, wherein the first selected area ofthe image comprises data to be protected, the data being derived from adata store.
 24. The non-transitory computer readable storage medium ofclaim 21, wherein the first selected area of the image comprises dataderived using a late binding schema.
 25. The non-transitory computerreadable storage medium of claim 21, wherein the blurred area is createdby: defining an area of the image that encompasses the second selectedarea; and executing a single blur operations using pixels in the definedarea as input to a blur filter, the blur filter being one of a Gaussianfilter, a motion filter, a box filter, a spin filter, or a zoom filter.26. The non-transitory computer readable storage medium of claim 21,wherein replacing the first selected area with the corresponding portionof the blurred area comprises: transforming the first selected area inthe image into a transparent area in a foreground layer; andsuperimposing the image with the transparent area on top of the blurredarea, wherein the blurred area is in a background layer.
 27. Thenon-transitory computer readable storage medium of claim 21, wherein theoperations further comprise: receiving a request to blur a differentselected area of the image, wherein the different selected areacomprises a sub-area that is not included in the first selected area;and replacing the different selected area of the image with a secondcorresponding portion of the blurred area.